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Seminário do Grupo de Lógica, Inteligência Artificial
e Métodos Formais - LIAMF
Seminário Registrado na CPG do IME/USP
Página: http://www.ime.usp.br/~liamf/seminarios/index.html
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Título: Anytime Lifted Belief Propagation
Palestrante: Rodrigo de Salvo Braz (SRI International)
Data: 29/4/2010, 14hs
Local: Sala 254-A
Lifted first-order probabilistic inference, which manipulates
first-order representations directly, has been receiving increasing
attention. To date, all lifted inference methods require a model to be
shattered against itself and evidence, before inference starts. In
many situations this produces a new model that is not far from
propositionalized, therefore canceling the benefits of lifted inference.
We present an algorithm, Anytime Lifted Belief Propagation, that
corresponds to this intuition by performing shattering during belief
propagation inference, on an as-needed basis, starting on the most
relevant parts of a model rst. The trade-off is having an (exact) bound
(an interval) on the query's belief rather than an exact belief. Bounds
are useful when approximate answers are sufficient and, in
decision-making applications, can even be enough to determine the
decision that would be picked from the exact belief. Moreover, the
bounds can be made to converge to the exact solution as inference and
shattering converge to the entire model. Interestingly, this algorithm
mirrors theorem-proving, helping to close the gap between probabilistic
and logic inference.
Todos são bem-vindos.
Seminário do Grupo de Lógica, Inteligência Artificial
e Métodos Formais - LIAMF
Seminário Registrado na CPG do IME/USP
Página: http://www.ime.usp.br/~liamf/seminarios/index.html
-----------------------------------------------------------------------------
Título: Anytime Lifted Belief Propagation
Palestrante: Rodrigo de Salvo Braz (SRI International)
Data: 29/4/2010, 14hs
Local: Sala 254-A
Lifted first-order probabilistic inference, which manipulates
first-order representations directly, has been receiving increasing
attention. To date, all lifted inference methods require a model to be
shattered against itself and evidence, before inference starts. In
many situations this produces a new model that is not far from
propositionalized, therefore canceling the benefits of lifted inference.
We present an algorithm, Anytime Lifted Belief Propagation, that
corresponds to this intuition by performing shattering during belief
propagation inference, on an as-needed basis, starting on the most
relevant parts of a model rst. The trade-off is having an (exact) bound
(an interval) on the query's belief rather than an exact belief. Bounds
are useful when approximate answers are sufficient and, in
decision-making applications, can even be enough to determine the
decision that would be picked from the exact belief. Moreover, the
bounds can be made to converge to the exact solution as inference and
shattering converge to the entire model. Interestingly, this algorithm
mirrors theorem-proving, helping to close the gap between probabilistic
and logic inference.
Todos são bem-vindos.
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